Abdollah Dehzangi

Molecular Biology · 2014–2018

A researcher in the Science corpus: 5 papers; trusts 5 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 57 citations into it; #8,046 of 17,773 by trust here, #5,405 by citations
• Materials Chemistry — 45 citations into it; #9,264 of 11,513 by trust here, #5,923 by citations
• Artificial Intelligence — 11 citations into it

Whom Abdollah Dehzangi trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Rasmus Berg Palm
2. Yoshua Bengio
3. J. Urgen Schmidhuber
4. Geoffrey E. Hinton
5. Eshel Faraggi
6. Guoli Wang
7. Yann LeCun
8. Jens Meiler
9. Burkhard Rost
10. Stephen F. Altschul

Who trusts Abdollah Dehzangi overall

1. Chaopeng Shen
2. Jack Hanson
3. Yuedong Yang
4. Seonwoo Min
5. Yujuan Gao
6. Rhys Heffernan
7. Debswapna Bhattacharya
8. Sheng Wang
9. Kuldip K. Paliwal

Papers from Abdollah Dehzangi's perspective

Ordered by how strongly the researchers Abdollah Dehzangi trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Long Short-Term Memory
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto‐encoder deep neural network (2014) — 152 citations
• Improving prediction of secondary structure, local backbone angles, and solvent accessible surface area of proteins by iterative deep learning (2015) — 346 citations
• SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks. (2017) — 149 citations
• Highly accurate sequence-based prediction of half-sphere exposures of amino acid residues in proteins (2016) — 99 citations
• SPIN2: Predicting sequence profiles from protein structures using deep neural networks (2018) — 101 citations